A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Paper studies ensemble probabilistic regression trees for smooth approximations.
Probabilistic-driven classification techniques extend the role of traditional approaches that output labels (usually integer numbers) only. Such techniques are more fruitful when dealing with problems where one is not interested in recognition/identification only, but also into monitoring the behavior of consumers and/…
A new method for detecting anomalies in large, high-dimensional data streams using probabilistic forest models.
Random Forests are reinterpreted as generative models to handle missing data and detect outliers.
GeFs use deep generative models to enhance prediction robustness and uncertainty.
This paper improves deep forest models with soft routing and topology learning.
Partition Tree estimates conditional densities for mixed continuous and categorical variables.
We derive Gaussian approximations for random forest predictions using region-based stabilization.
Real world systems typically feature a variety of different dependency types and topologies that complicate model selection for probabilistic graphical models. We introduce the ensemble-of-forests model, a generalization of the ensemble-of-trees model. Our model enables structure learning of Markov random fields (MRF) …
Novel unsupervised random forests improve density estimation and data synthesis.
We propose probabilistic models that can extrapolate learning curves of iterative machine learning algorithms, such as stochastic gradient descent for training deep networks, based on training data with variable-length learning curves. We study instantiations of this framework based on random forests and Bayesian recur…
Coordinate ascent variational inference is an important algorithm for inference in probabilistic models, but it is slow because it updates only a single variable at a time. Block coordinate methods perform inference faster by updating blocks of variables in parallel. However, the speed and stability of these algorithms…
Tractable yet expressive density estimators are a key building block of probabilistic machine learning. While sum-product networks (SPNs) offer attractive inference capabilities, obtaining structures large enough to fit complex, high-dimensional data has proven challenging. In this paper, we present random sum-product …
Combines coarse learners for nonparametric probabilistic regression.
CNNs improve wind speed forecasts in the Netherlands.
Paper introduces probabilistic forecasting methods for cryptocurrency volatility.
Simplifies RF predictions by focusing on a subset of nearest neighbors.
The paper proposes a method to improve random forest classification accuracy by weighting trees based on their decision path reliability.
A new type of distributional regression tree uses soft split rules for better predictive performance.
Predictive modelling and supervised learning are central to modern data science. With predictions from an ever-expanding number of supervised black-box strategies - e.g., kernel methods, random forests, deep learning aka neural networks - being employed as a basis for decision making processes, it is crucial to underst…
A reliable and accurate forecasting model for crop yields is of crucial importance for efficient decision-making process in the agricultural sector. However, due to weather extremes and uncertainties, most forecasting models for crop yield are not reliable and accurate. For measuring the uncertainty and obtaining furth…
Bayesian Decision Trees are known for their probabilistic interpretability. However, their construction can sometimes be costly. In this article we present a general Bayesian Decision Tree algorithm applicable to both regression and classification problems. The algorithm does not apply Markov Chain Monte Carlo and does…
A grand challenge in machine learning is the development of computational algorithms that match or outperform humans in perceptual inference tasks that are complicated by nuisance variation. For instance, visual object recognition involves the unknown object position, orientation, and scale in object recognition while …
Paper introduces ps-BART for estimating nonlinear ATE and CATE in continuous treatments.
iMondrian forest combines isolation forest and Mondrian forest for better anomaly detection.
Ensembles of randomized decision trees, usually referred to as random forests, are widely used for classification and regression tasks in machine learning and statistics. Random forests achieve competitive predictive performance and are computationally efficient to train and test, making them excellent candidates for r…
In Business Intelligence, accurate predictive modeling is the key for providing adaptive decisions. We studied predictive modeling problems in this research which was motivated by real-world cases that Microsoft data scientists encountered while dealing with e-commerce transaction fraud control decisions using transact…
New random forest method provides optimal rates and confidence bands.
RFpredInterval package builds prediction intervals for random forests and boosted forests.
We propose random hinge forests, a simple, efficient, and novel variant of decision forests. Importantly, random hinge forests can be readily incorporated as a general component within arbitrary computation graphs that are optimized end-to-end with stochastic gradient descent or variants thereof. We derive random hinge…
Improved random forest proximities capture data geometry.
Deep forests enhance expressiveness exponentially with depth, not width or tree size.
This paper improves forest pruning to balance accuracy and interpretability.
This paper is a comment on the survey paper by Biau and Scornet (2016) about random forests. We focus on the problem of quantifying the impact of each ingredient of random forests on their performance. We show that such a quantification is possible for a simple pure forest , leading to conclusions that could apply more…
Forest-guided smoothing uses random forest outputs for interpretable local smoothers.
In this paper we propose using the principle of boosting to reduce the bias of a random forest prediction in the regression setting. From the original random forest fit we extract the residuals and then fit another random forest to these residuals. We call the sum of these two random forests a \textit{one-step boosted …
P-SE explains model decisions with minimal feature subsets and fast estimators.
Fault detection in industrial plants is a hot research area as more and more sensor data are being collected throughout the industrial process. Automatic data-driven approaches are widely needed and seen as a promising area of investment. This paper proposes an effective machine learning algorithm to predict industrial…
By seeking the narrowest prediction intervals (PIs) that satisfy the specified coverage probability requirements, the recently proposed quality-based PI learning principle can extract high-quality PIs that better summarize the predictive certainty in regression tasks, and has been widely applied to solve many practical…
Improves time series classification with forest proximities.
Random forests reduce bias and variance, especially in low SNR settings.
New random forest variants achieve optimal performance in high dimensions.
Enhances random forest consistency and introduces DMRF for improved performance.
Online random forests improve Q-learning performance in specific gym environments.
Quantile regression improves urban water demand forecasting.
The increased usage of solar energy places additional importance on forecasts of solar radiation. Solar panel power production is primarily driven by the amount of solar radiation and it is therefore important to have accurate forecasts of solar radiation. Accurate forecasts that also give information on the forecast u…
New method learns representations for decision forests using input perturbation.
Improved random forest models enhance machine learning predictions.